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Yu Wang, Hong-Qing Wang, Lei Han, Yin-Jing Lin, and Yan Zhang

1. Introduction Nowcasting is a hot issue in the field of mesoscale meteorology. Nowcasting techniques can be divided into three classes ( Wilson et al. 1998 ): 1) extrapolation techniques, 2) numerical prediction techniques, and 3) expert systems with conceptual models. Extrapolation methods based on radar echoes are robust and have better forecast skill than do numerical prediction methods in short-term quantitative precipitation forecasting ( Lin et al. 2005 ; Wilson et al. 2010 ). Linear

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Timothy J. Hall, Carl N. Mutchler, Greg J. Bloy, Rachel N. Thessin, Stephanie K. Gaffney, and Jonathan J. Lareau

, artificial intelligence (AI), cognitive psychology, engineering, and knowledge discovery in databases. These techniques are usually applied in very short-range forecasting from 0 up to approximately 6 h in the future ( Bankert and Hadjimichael 2007 ; Hansen 2007 ; Vislocky and Fritsch 1997 ). Each algorithm chosen for this investigation was implemented to produce 1-, 2-, 3-, 4-, and 5-h probabilistic forecasts of cloud-free (i.e., clear) sky condition for six areas of regard (AORs) representing

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Joao Gari da Silva Fonseca Jr., Fumichika Uno, Hideaki Ohtake, Takashi Oozeki, and Kazuhiko Ogimoto

and gradient boosting regression-related forecasts (maximum RMSE reduction of 18% in one location and maximum RMSE increase of 7.1% in another). Currently, the potential benefits of machine-learning techniques that use random projections of the inputs into high-dimensional space, such as extreme learning machines (ELM) and reservoir computing, are also being explored in the problem of PV power forecasts. Such techniques can also be extended to the problem of solar irradiation forecasts. The main

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Xiaomin Chen, Kun Zhao, Wen-Chau Lee, Ben Jong-Dao Jou, Ming Xue, and Paul R. Harasti

. (1994) proposed a robust single-Doppler wind retrieval technique, called the velocity track display (VTD), to deduce the primary circulations of TCs at different altitudes in real time from an airborne tail Doppler radar on board the National Oceanic Atmospheric Administration WP-3D aircraft. To study the landfalling TCs using coastal radars, Lee et al. (1999) reformulated the VTD equations for a ground-based Doppler radar, called the ground-based VTD (GBVTD). Recently, successful applications of

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Yan Guo, Jianping Li, and Jiangshan Zhu

-month-run ensemble forecasts by the NCEP Climate Forecast System, version 2 (CFSv2), covering a 13-yr period of 2001–13 from retrospective forecast (2001–10) and operational forecast (2011–13) were employed. CFSv2, the second version of the fully coupled dynamic seasonal forecast system, consists of the GFS at T126 resolution, the Modular Ocean Mode, version 4 (MOM4), at 0.25° × 0.5° grid coupled with a two-layer sea ice model, and the four-layer Noah land surface model. This system generated real

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Thomas Nehrkorn and Ross N. Hoffman

cases, it may be necessary to include a scaling of the error magnitudes by a tunable constant or to include the observed correlation between displacement magnitudes and gradients of the forecast fields in the simulation procedure. b. Comparison of FCA and dynamic ensemble forecasts over North America 1) Procedure Based on the generally successful consistency check for S2, the FCA pseudoensemble technique was compared with a set of ECMWF 51-member, 1–10-day, 500-hPa geopotential height ensemble

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David P. Duda and Patrick Minnis

numerical weather model data to make probabilistic forecasts of weather variables for many years. One of the earliest models reported in the literature was developed by Lund (1955) , and the model output statistics (MOS) method ( Glahn and Lowry 1972 ) provided some of the first widely used probabilistic forecasts developed from numerical weather forecasts. By using a statistical technique such as logistic regression, forecasts of the occurrence or nonoccurrence of a weather-related event can be

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Robert D. Sharman, Yubao Liu, Rong-Shyang Sheu, Thomas T. Warner, Daran L. Rife, James F. Bowers, Charles A. Clough, and Edward E. Ellison

1. Introduction Part I of this series of papers ( Liu et al. 2008a ) provides an overview of an operational mesogamma-scale forecast model, called the Real-Time Four-Dimensional Data Assimilation (RTFDDA) system, that is in use at the U.S. Army Test and Evaluation Command (ATEC) test ranges. The forecast component of RTFDDA is based on the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; Grell et al. 1995 ). The forecast model

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O. Kipkogei, A. M. Mwanthi, J. B. Mwesigwa, Z. K. K. Atheru, M. A. Wanzala, and G. Artan

forecasts typically have low spatial resolution and to generate increased spatial detail, statistical or dynamical downscaling techniques are employed. In the dynamical technique, regional models, driven by initial and boundary conditions from GCMs, are used ( Castro et al. 2006 ). This technique is the more fundamental in that it involves simulation of the dynamical and physical processes giving rise to spatial details. However, imperfect representation of such processes may introduce errors that limit

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Iris Odak Plenković, Luca Delle Monache, Kristian Horvath, and Mario Hrastinski

filter in analog space (KFAS); Delle Monache et al. 2011 ]. With that approach, the correction of the current forecast is based on a higher weight to the analog forecasts closer to it. The authors demonstrate that both approaches increase correlation and reduce random and systematic errors. Similar approaches are used for predicting other variables as well. Djalalova et al. (2015) show similar results predicting PM 2.5 concentration, while Nagarajan et al. (2015) test the techniques across

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